Federated learning (FL) has received significant of attention for its ability to train large-scale models in a decentralized manner without requiring direct access to user data [1]}, [2]}. It also has been increasingly applied to facilitate decision-making in various crucial areas, such as healthcare, recruitment, loan grading, etc. Nevertheless, there are concerns regarding biased performance against certain populations in such ML-assisted decision-making systems [3]}, [4]}.
Despite the increasing attention on FL fairness, most existing studies [5]}, [6]} focus on equalizing the performance across different participating devices/silos. Few works have discussed the group fairness [7]} related to sensitive attributes (e.g., gender, race), which is a crucial requirement for responsible AI systems aiming to ensure the model treats the groups defined by sensitive attributes equally.
